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Author: Admin | 2025-04-28
Less explored in the learning-to-rank setting. In this paper, we lay the groundwork for intrinsically interpretable learning-to-rank by introducing generalized additive models (GAMs) into ranking tasks. Generalized additive models (GAMs) are intrinsically interpretable machine learning models and have been extensively studied on regression and classification tasks. We study how to extend GAMs into ranking models which can handle both item-level and list-level features and propose a novel formulation of ranking GAMs. To instantiate ranking GAMs, we employ neural networks instead of traditional splines or regression trees. We also show that our neural ranking GAMs can be distilled into a set of simple and compact piece-wise linear functions that are much more efficient to evaluate with little accuracy loss. We conduct experiments on three data sets and show that our proposed neural ranking GAMs can outperform other traditional GAM baselines while maintaining similar interpretability. Improving Cloud Storage Search with User Activity Rolf Jagerman Weize Kong Rama Kumar Pasumarthi Zhen Qin Michael Bendersky Marc Najork Cloud-based file storage platforms such as Google Drive are widely used as a means for storing, editing and sharing personal and organizational documents. In this paper, we improve search ranking quality for cloud storage platforms by utilizing user activity logs. Different from search logs, activity logs capture general document usage activity beyond search, such as opening, editing and sharing documents. We propose to automatically learn text embeddings that are effective for search ranking from activity logs. We develop a novel co-access signal, i.e., whether two documents were accessed by a user around the same time, to train deep semantic matching models that are useful for improving the search ranking quality. We confirm that activity-trained semantic matching models can improve ranking by conducting extensive offline experimentation using Google Drive search and activity logs. To the best of our knowledge, this is the first work to examine the benefits of leveraging document usage activity at large scale for cloud storage search; as such it can shed light on using such activity in scenarios where direct collection of search-specific interactions (e.g., query and click logs) may be expensive or infeasible. SESSION: Session 13: Knowledge Temporal Cross-Effects in Knowledge Tracing Chenyang Wang Weizhi Ma Min Zhang Chuancheng Lv Fengyuan Wan Huijie Lin Taoran Tang Yiqun Liu Shaoping Ma Knowledge tracing (KT) aims to model students' knowledge level based on their historical performance, which plays an important role in computer-assisted education and
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